Principal component analysis of cyclic spectrum features in automatic modulation recognition

Automatic modulation recognition is a rapidly evolving area of signal analysis. In recent years, interest from the academic and military research institutes has focused around the research and development of modulation recognition algorithms. Any communication intelligence (COMINT) system comprises three main blocks: receiver front-end, modulation recogniser and output stage. Considerable work has been done in the area of receiver front-ends. The work at the output stage is concerned with information extraction, recording and exploitation and begins with signal demodulation, that requires accurate knowledge about the signal modulation type. There are, however, two main reasons for knowing the current modulation type of a signal; to preserve the signal information content and to decide upon the suitable counter action, such as jamming. Automatic Modulation Recognition of Communications Signals describes in depth this modulation recognition process. Drawing on several years of research, the authors provide a critical review of automatic modulation recognition. This includes techniques for recognising digitally modulated signals. The book also gives comprehensive treatment of using artificial neural networks for recognising modulation types. Automatic Modulation Recognition of Communications Signals is the first comprehensive book on automatic modulation recognition. It is essential reading for researchers and practising engineers in the field. It is also a valuable text for an advanced course on the subject.

Intelligent radios collect information by sensing signals within the radio spectrum, and the automatic modulation recognition (AMR) of signals is one of their most challenging tasks. Although the result of a modulation classification based on a deep neural network is better, the training of the neural network requires complicated calculations and expensive hardware. Therefore, in this paper, we propose a master–slave AMR architecture using the reconfigurability of field-programmable gate arrays (FPGAs). First, we discuss the method of building AMR, by using a stack convolution autoencoder (CAE), and analyze the principles of training and classification. Then, on the basis of the radiofrequency network-on-chip architecture, the constraint conditions of AMR in FPGA are proposed from the aspects of computing optimization and memory access optimization. The experimental results not only demonstrated that AMR-based CAEs worked correctly, but also showed that AMR based on neural networks could be implemented on FPGAs, with the potential for dynamic spectrum allocation and cognitive radio systems.

Automatic modulation recognition is very important for communication intelligence applications such as signal interception for satellite communications, civil authority, surveillance and threat analysis. Automatic digital modulation recognition is a system that automatically classifies the modulation type of the intercepted signal. This paper presents a method for the automatic recognition of digital modulations without a priori knowledge of the signal parameters. This algorithm works at low signal to noise ratio and get nearly to 100 % of correct classification percentage at 0 dB which is effective in Electronic warfare and communication surveillance applications. This method can recognize common single-carrier modulated satellite communication signals such as M-ary phase-shift keying, M-ary quadrature amplitude modulation and M-ary amplitude phase shift keying which used in DVB-S2. This algorithm is performed by a sequential steps to blindly classify the signal using PSD of instantaneous amplitude, kurtosis and HOS (cumulants).

With the deepening development of communication technology, the technology of automatic modulation and recognition of communication signals has been more and more widely used in military and civilian fields. This paper mainly studies the implementation of automatic modulation recognition using deep learning as a computing tool, focusing on CNN neural network and LSTM neural network, and conducting simulation experiments on public data sets. Based on the original CNN neural network, this paper introduces the structure of LSTM neural network and combines the advantages of the two types of neural networks to explore a combined neural network that is superior to the originally used CNN network. The experimental results of this thesis show that introducing the features of dynamic time series modeling of LSTM networks into deep learning networks can capture the global and local information of signals more effectively and improve the accuracy of neural networks in automatic modulation recognition.

In this paper, a method is presented based on the cyclic spectral features and the neural network classifiers for performing automatic modulation recognition. The process of the automatic modulation recognition using the method is divided into three basic steps: cyclic spectrum analysis, feature extraction and neural network classifier. Because a new method called as maximum likelihood filter method is used to estimate cyclic spectral density of communication signal, better estimate performance can be acquired in the scene of short length of data. Probability neural network algorithm improves the performance of classifier. Some computer simulation results showing the performance of the method in this paper are improved. The method can efficiently recognize almost all currently used modulation types and the recognition accuracy rate is over 95% at the SNRs of 10 dB.

Wavelet transform-based methodologies for both Automatic Modulation Recognition (AMR) and Demodulation of digitally modulated communications signals can be utilized in an enabling platform for the implementation of a new class of communications systems. In particular, such techniques could enable the development of agile radio transceivers for use in both commercial and military applications. Such radio transceivers would have the ability to transmit and receive signals using many different modulation schemes while employing a common receiver architecture based on a single demodulator. In this dissertation, the development of AMR and Demodulation techniques are based on the relatively new mathematical theory of Wavelet Transforms (WTs). Information-bearing signals acquired by communications receivers are transformed into the wavelet-domain using the Continuous Wavelet Transform (CWT) and then applied to signal processing algorithms that also use the CWT in conjunction with pattern recognition techniques. In particular, the method of template-matching is used for both the AMR and Demodulation processes. Signal templates characterizing various modulated signals are used for both processes. The signal templates are determined based on the signal features present in the fractal patterns of their corresponding scalograms for specific modulation schemes as they appear in the wavelet-domain. The algorithms developed in this work are capable of both classifying the method of modulation used in the acquired signal, as well as subsequently automatically demodulating the signal to recover the message. The classes of digitally modulated signals considered in this work include variants of the Amplitude-, Frequency-, Phase-Shift Keying modulation families, i.e., ASK, FSK, and PSK, respectively, and multiple-level Quadrature Amplitude Modulation (M-ary QAM) families. The AMR and Demodulation performances are evaluated in the presence of Additive White Gaussian Noise (AWGN) over a wide range of Signal-to-Noise Ratio (SNR) values. Through extensive Monte Carlo computer simulations it is determined that the average correct classification rates using wavelet-based AMR for PSK, ASK, and QAM are over 98%, and over 90% for FSK signals, all at an SNR of 0 dB. The Bit Error Rate (BER) performance obtained using wavelet-based Demodulation is at least one order of magnitude better than the matched filter-based BER performance realized for the modulation schemes considered.

The scientific article is devoted to the issues of SDR system control. Software Defined Radio is a system designed for software control of information transmission processes in a radio communication channel. Recognition of digital modulation types is used, which automatically classifies the type of digital modulation of the received signal. The following issues are covered in the article: the analysis of existing approaches in the task of automatic recognition of types of digital modulation is carried out; the analysis and classification of informative features in the task of automatic recognition is carried out the following types of digital modulation: 2-PSK, 4-PSK, 8-PSK, 2-FSK, 8-QAM, 16-QAM, 64-QAM and OFDM. This article uses a neural network approach based on cumulative characteristics. To solve these problems, the methods of calculation and selection of informative cumulative features described in the developed model of the system of automatic recognition of digital modulation types by means of a neural network on cumulative features at a known value of carrier frequency are described. A model of the system of automatic recognition of digital modulation types at a known value is constructed carrier frequency. An algorithm for automatic recognition of digital modulation types has been developed using a multilayer neural network. The influence of noise in the communication channel on the probability of recognizing the types of digital modulation at a known value of the carrier frequency is investigated. It was found that regardless of the type of noise in the communication channel, the law of error distribution in IQ data becomes close to normal. This fact is one important argument for the use of cumulative features in the task of automatic recognition of types of digital modulation. Therefore, the task of automatic recognition of digital modulation types is quite relevant. Further research may be aimed at expanding the range of high-order cumulative features used, due to which it is possible to increase the probability of correct recognition of types of digital modulation, and solving the recognition problem at an unknown value of the frequency and initial phase of the carrier signal.

In this paper, we consider estimation of the carrier frequency of a single-tone digitally modulated signal in the automatic modulation recognition (AMR) environment. This issue is quite different from that in the cooperative communications systems since the AMR receiver works in a non-cooperative mode with the transmitter. That is, the AMR receiver does not have any priori knowledge of the transmitted signal such as symbol rate, shaping pulse, modulation type, and constellation size. A blind carrier frequency estimation algorithm is presented in this paper based on the phase information of the autocorrelations of the received signal. The phase distribution has been analyzed, and an iterative mechanism has been employed to improve the accuracy of carrier frequency estimation. Simulations show that the algorithm works well for amplitude-shift keying (ASK), phase-shift keying (PSK), and quadrature amplitude modulation (QAM) signals.

Automatic modulation recognition (AMR) stands as a crucial core technology within the realm of signal processing and perception, playing a significant part in harsh electromagnetic environments. The time–frequency image (TFI) of communication signals can manifest modulation characteristics and serve as a foundation for signal modulation recognition and classification. However, under the influence of the electromagnetic environment, communication signals are exposed to varying degrees of interference, which poses a challenge to the recognition of modulation types. Taking into account the effects of interference and channel fading, this paper introduces a communication signal modulation recognition algorithm based on deep learning (DL) and time–frequency analysis. This approach employs short-time Fourier transform (STFT) to generate time–frequency diagrams from time-domain signals. Subsequently, it binarizes the image and feeds it as input data to the neural network. Our research presents a composite deep convolutional neural network (CNN) architecture known as the composite dense-residual neural network (CDRNN). This architecture focuses on enhancing the feature extraction and identification, aiming to achieve accurate recognition of modulation types in harsh electromagnetic environments. Finally, simulation results validate that the proposed deep learning algorithm holds remarkable advantages in boosting the accuracy of modulation type recognition with better adaptability. The algorithm shows better performance even in harsh electromagnetic environments. When the signal-to-noise ratio (SNR) is 18 dB, the recognition accuracy can reach 92.1%.

Deep learning has significantly advanced automatic modulation recognition (AMR), yet existing deep learning-based AMR methods struggle to adapt to the rapid evolution of 6G and beyond. A key challenge is their inability to incrementally integrate new modulation types without catastrophic forgetting of previously learned ones. Additionally, the annotation of wireless signals remains difficult, further limiting adaptability. To address these challenges, we introduce few-shot class-incremental learning for AMR (FSCIL-AMR) and propose the orthogonal pseudo-prototype and pseudo-target (O3PT) method. O3PT generates orthogonal pseudo-prototypes to replace ambiguous class prototypes caused by high signal feature similarity, thereby mitigating classification confusion when computing cosine similarities between features and prototypes. It also implicitly preserves feature space for future modulation types and reformulates complex prototype constraints into a more manageable alignment task. Furthermore, O3PT exploits inter-class feature similarities to generate pseudo-targets and utilizes supervised contrastive learning to impose adaptive constraints, enhancing inter-class separability while mitigating overfitting caused by limited incremental training signals. By training a lightweight projector, O3PT enables efficient network expansion as new modulation types emerge. Extensive experiments on the RML2018.01A and RML2016.10A datasets validate its effectiveness.

In communication signal recognition, there are problems such as a tedious feature extraction process and low applicability of extracted features. This paper simulates wireless communication channels and suggests an algorithm that uses nearest neighbor component analysis (NCA) along with convolutional neural networks (CNN) for classification. The algorithm chooses wavelet entropy (WE), wavelet approximate energy ratio (WAER), and the first 2–4 singular values as the core features. Eight different forms of modulations, including GFSK, CPFSK, B-FM, DSB-AM, SSB-AM, BPSK, QPSK and PAM4 would be automatically classified using the technique. According to the experiment results, the average recognition accuracy for the eight signals is 93.6% when the signal-to-noise ratio is 30dB. In addition, this paper also discusses the results and accuracy of the model to identify 6 and 10 types of signal modulation and studies the accuracy of the recognition under different signal-to-noise ratios, verifying the robustness of the model.

Modulation recognition of non-cooperation underwater acoustic communication signals using principal component analysis

An adaptive procedure for automatic modulation recognition is described. With it the automatic modulation classification and recognition of radio communication signals with a priori unknown parameters is possible effectively. The results of modulation recognition are important in the context of radio monitoring or electronic support measurements. The special features of the procedure are the possibility to adapt it dynamically to nearly all modulation types, and the capability to recognize continuous phase modulation (CPM) signals like Gaussian minimum-shift keying (GMSK) too. A time synchronization to the symbol rate is not necessary.

An algorithm based on a decision-tree approach for the automatic modulation recognition of communications signals is presented. This algorithm can discriminate between continuous wave (CW), amplitude modulation (AM), double sideband suppressed carrier (DSB-SC), frequency modulation (FM), frequency shift keying (FSK), binary phase shift keying (BPSK) and quaternary phase shift keying (QPSK) modulations. Requirements for a priori knowledge of the signals are minimized by the inclusion of an efficient carrier frequency estimator and low sensitivity to variations in the sampling epochs. Computer simulations indicate good performance on an AWGN channel, even at signal-to-noise ratios as low as 5 dB. This compares favorably with the performance obtained with most algorithms based on pattern recognition techniques.

In the field of electronic reconnaissance, due to the complex electromagnetic environment, the receiver receives the high density signals, and two or even more radar signals are often overlapped. However, the features of the signals are completely interleaved. Especially under low signal-to-noise ratio (SNR), the signals become weak and messy, and the automatic modulation recognition is quite difficult. Through Fourier transform, the overlapping signals can be separated in frequency domain to some extent. The Autocorrelation Function (ACF) has some effect on de-noising, and the time-frequency analysis can reflect the instantaneous frequency of the signal. Therefore, this paper proposes a Multi-Domain Squeeze-and-Excitation ResNeXt (SE-ResNeXt) based on multi-branch feature fusion. In addition to the time domain, the signal is transformed to frequency domain, autocorrelation domain and time-frequency domain, and the high-dimensional feature vectors of signals in different domains are extracted by SE-ResNeXt. Finally, the Squeeze-and-Excitation (SE) block fuses the high-dimensional features of the four branches, the automatic modulation recognition of the overlapping radar signal is realized. The simulation results of six typical radar signals show that the algorithm is robust and effective. At -4dB, the recognition accuracy of the proposed method is more than 92%. Compared with other methods, the proposed method can achieve better performance under low SNR.